Advanced Techniques for Missing Data Handling

Vyshnavi Nammi, Dheeraj Reddy Podduturi, Ruchith Chippari, Anna Dragotta

Introduction

Missing Data: Missing data refers to missing values for certain observations or variables in a dataset.

Types of Missing Data

  1. Missing Completely at Random (MCAR)

  2. Missing at Random (MAR)

  3. Missing Not at Random (MNAR)

Data Imputation: It is a technique that aims to replace the missing data with a substitute value.

Missing Completely at Random (MCAR)

Missing at Random (MAR)

Missing Not at Random (MNAR)

Importance of Missing Data

Handling missing data helps to prevent biased or sub-optimal outcomes and mishandling leads to inaccuracy of analysis.

Impact of Missing Data on Statistical Analysis

Reduced statistical power and invalid conclusions


Methods of Handling Missing Data: Imputation and Data Removal

Other Applications: Electronic Health Record (EHR)

The EHR has increasingly become used for data mining and analysis for a variety of health conditions. However, due to irregular observation times and innate uncertainties in a medical setting, the EHR datasets are missing values. The EHR systems were not created in mind for research. Researchers who do use this data may categorize this missing data as missing completely at random, missing at random, or not missing at random.

Methods

Types of Imputation Methods

  • Mean/Median Imputation

  • Multiple Imputation

  • KNN Imputation

  • Most Frequent Value Imputation

Mean and Median Imputation Methods

This is simple single imputation method, one will take the average or common value and fill in for those missing values.

Figure 1: Mean/Median Imputation Normal Distribution. Image by Arun Amballa (2020) via medium.com

Mean Imputation

  1. Mean Imputation Suitable for normal distributions
  2. Data Missing at random
  3. Leads to biased estimates.

Median Imputation

  1. Median imputation is suitable for skewed distributions.
  2. It can lead to decreased variance and standard deviation

Multiple Imputation

  • Generate multiple sets of plausible values for each missing data point.

  • Package used in this method is mice (Multivariate Imputation by Chained Equations)

    Figure 2: Missing data value replaced by several different values. Image by Martijn W Heymans and Iris Eekhout (2019) via bookdown.org

Steps Involved in Multiple Imputation Technique:

Imputation : generate imputed datasets, where missing values are filled using a specified imputation method.

Analysis : analyze each imputed dataset separately using the desired statistical analysis.

Pooling: combine the results from the analyses to obtain final estimates and standard errors.

K-Nearest Neighbors (KNN) Imputation

  • Impute missing values based on the values of their nearest neighbors in the dataset.

  • The impute.knn() function in the impute package is a popular choice for KNN imputation.

  • Method: impute missing values by averaging or using the majority vote of the (k) nearest neighbors in the feature space.

  • Application: effective for imputing values based on similarities in multivariate space.

Most Frequent Value Imputation

  • Also known as mode imputation

  • Method: replace missing values with the most frequently occurring value in the variable.

  • Application: appropriate for categorical variables or when missing values are likely to be the mode.

Analysis and Results

Data Description

  • Titanic dataset: 809 fatalities, 465 survivors, missing data in various fields.

  • Class distribution: 200 in Class 1, 119 in Class 2, 181 in Class 3.

  • Missing data: Gender, fare, cabin, embarkation port, lifeboat, body ID, destination.

Fields in Titantic Data Set

Name: The name of the passenger. Sex: Gender of the passenger.
Age: Age of the passenger.
Sibsp: Number of siblings or spouses aboard.
Parch: Number of parents or children aboard.
Ticket: Ticket number. Fare: Fare paid for the ticket.
Cabin: Cabin number.
Embarked: Port of embarkation.
Boat: Lifeboat assignment.
Body: Identification number of the recovered body.
Home.Dest: Home or destination of the passenger.

Percentage of Missing data

Function used: vis_mis

Missing Element in Each Column

Pattern of Missing Data

  • Significant missing data in body (91%), cabin (77%), and boat (63%) variables.

  • Body variable contains the highest missing percentage, followed by cabin and boat.

  • Variable types and influencing factors crucial for effective imputation methods.

Mean/Median Imputation

The code involves installing and loading the “tidyverse” package, which provides a variety of tools for data science jobs, as well as loading the “readxl” package for handling Excel files.The code indicates that 1014 m and 263 are missing values in “age”

{r} # Print the counts of missing values cat("Missing values in 'age':", missing_age, "\n") #Missing values in 'age': 263}

The code shows that there are 823 missing values in the “boat” and 1014 missing values in the “cabin.”

{r} cat("Missing values in 'cabin':", missing_cabin, "\n") #Missing values in 'cabin': 1014}

The code shows 1188 missing values in the “body” and 823 missing values in the “boat.”

{r} cat("Missing values in 'body':", missing_body, "\n") #Missing values in 'body': 1188}

{r} cat("Missing values in 'boat':", missing_boat, "\n") #Missing values in 'boat': 823}

The code shows 564 missing values in the “home.dest”

{r} cat("Missing values in 'home.dest':", missing_home_dest, "\n") #Missing values in 'home.dest': 564}

Multiple Imputation

Using the below code: - Import dataset, Select columns for imputation, Use Predictive mean matching for multiple imputation and save imputed data.

Use random forest and logistic regression techniques to show summary statistics for the imputed data.

K-Nearest Neighbors (KNN) Imputation

Functions used:- aggr Package used:- VIM

Above functions and packages used to display missing data pattern after loading.

White indicates missing.

Most Frequent Value Imputation

- Defines columns that have missing data

- Imputation is performed using the column with the highest frequency value.

- After imputation, it shows the total number of missing values for every column.

Data Visualization

Scatterplot Matrix:

  • Packages installed: GGally

  • Functions used: ggpairs

  • It chooses numerical variables and construct a scatter plot matrix for analysis.

Multiple Correspondence Analysis (MCA) Plot:

These are the results of Multiple Correspondence Analysis (MCA) on the Titanic dataset using the FactoMineR package.

Violin Plots Before and After Imputation:

This code depicts entire distribution of numeric data between titanic variables.

Conclusion

  • R-scripts explore diverse imputation techniques like KNN, Most Frequent Value, and Multiple Imputation for Titanic dataset.

  • Analysis includes detecting missing data patterns, imputed values, and pre-imputation data distribution variations.

  • Techniques include mean/median, KNN, multiple imputation, and random forest.

  • Emphasizes sensitivity analysis and validation for reliable imputation results.

Conclusion

  • R-Language proves effective and user-friendly in facilitating robust statistical analyses, positioning researchers at the forefront of advancements.

  • Provides researchers with practical skills for missing data handling and highlights emerging trends in the field.